Mathew Michelson and Craig A. Knoblock

1. Unsupervised Information Extraction from Unstructured, Ungrammatical Data Sources on the World Wide Web Mathew Michelson and Craig A. Knoblock

2. Abstract • Extracting unstructured data is difficult • Traditional methods do not apply • Solution: Unsupervised extraction • Results are competitive to supervised methods

3. Introduction • Web data could be useful if extracted (i.e Craigslist)

4. Introduction • Posts are not structured • The Phoebus method works on this data • But it requires much user input (supervised) • The paper presents and optional unsupervised method • This work extends on unsupervised semantic annotation

5. Introduction • Current work on UIE relies on redundancy • This approach does not use structural assumptions • This approach relies on similarity no redundancy • This approach creates relational data

6. Unsupervised Extraction Steps of the algorithm: • Automatically choosing the Reference Set • Matching Posts to the Reference Set • Unsupervised Extraction

7. Unsupervised Extraction • Automatically choosing the Reference Sets - They choose a reference set based on similarity - They calculate a similarity score and sort the sets - They use percent difference and average score - The algorithm scales linearly with size - They use multiple metrics as similarity score

8. Unsupervised Extraction • Matching Posts to the Reference Set - A vector-space model is used to match posts - The Jaro-Winkler metric is used to match tokens - Attributes that do not agree are removed - Now we can query the posts (Yay !)

9. Unsupervised Extraction • Unsupervised Extraction - A baseline is created between extracted field and reference set field - We remove tokens based on the baseline

10. Experimental Results Reference Sets Post Sets

11. Experimental Results Jensen-Shannon similarity

12. Experimental Results TF/IDF similarity

13. Experimental Results Jaccard similarity

14. Experimental Results Jaro-Winkler TF/IDF similarity

15. Experimental Results Results

16. Experimental Results Dice similarity

17. Experimental Results Jaccard similarity

18. Experimental Results TF/IDF similarity

19. Experimental Results Dice vs Phoebus

20. Experimental Results Jaro-Winkler vs Smith-Waterman

21. Experimental Results Comparison with other methods

22. Related Work • SemTag is a similar system • But it uses a crafted taxonomy • In contrast, SemTag focuses on disambiguation • CRAM is also similar but it requires labeling

23. Conclusion • This paper introduces an unsupervised information extraction technique • The Jensen-Shannon distance metric is better • Using text acronyms would be beneficial • Entity extraction could be a good idea

24. Questions